Explaining outliers occurrence and mechanism of their occurrence can be extremely important in a variety of domains. Malfunctions, frauds, threats, in addition to being correctly identified, oftentimes need a valid explanation in order to effectively perform actionable counteracts. The ever more widespread use of sophisticated Machine Learning approach to identify anomalies make such explanations more challenging. We present the Decision Tree Outlier Regressor (DTOR), a technique for producing rule-based explanations for individual data points by estimating anomaly scores generated by an anomaly detection model. This is accomplished by first applying a Decision Tree Regressor, which computes the estimation score, and then extracting the relative path associated with the data point score. Our results demonstrate the robustness of DTOR even in datasets with a large number of features. Additionally, in contrast to other rule-based approaches, the generated rules are consistently satisfied by the points to be explained. Furthermore, our evaluation metrics indicate comparable performance to Anchors in outlier explanation tasks, with reduced execution time.

翻译：解释异常的发生及其发生机制在多个领域中至关重要。故障、欺诈、威胁等除了需要被正确识别外，往往还需要有效的解释，以便有效实施可操作的应对措施。采用日益广泛的复杂机器学习方法识别异常，使得这类解释更具挑战性。我们提出决策树异常回归器（DTOR），这是一种通过对异常检测模型生成的异常分数进行估计，为单个数据点生成基于规则的解释的技术。该方法首先应用决策树回归器计算估计分数，然后提取与数据点分数相关的相对路径。我们的结果证明，即使在具有大量特征的数据集中，DTOR也表现出鲁棒性。此外，与其他基于规则的方法相比，待解释的数据点始终满足生成的规则。进一步地，我们的评估指标表明，在异常解释任务中，DTOR在降低执行时间的同时，性能与Anchors方法相当。